论文标题
通过DNN的积极学习：基于自动模拟数据集的自动化工程设计优化的流体动力学优化
Active Learning over DNN: Automated Engineering Design Optimization for Fluid Dynamics Based on Self-Simulated Dataset
论文作者
论文摘要
优化流体动力性能是一项重要的工程任务。传统上，专家根据经验估计设计形状，并通过昂贵的实验对其进行验证。无论是在时间和空间方面，这个昂贵的过程都可能仅探索数量有限的形状并导致次优设计。在这项研究中，应用了测试的深度学习体系结构来预测各种限制下的性能，并通过优化学习的预测功能来寻找更好的形状。主要的挑战是大量数据点深神经网络（DNN）的需求，这是可以模拟的。为了解决这一缺点，使用了常见的主动学习来探索DNN预测有希望的输出空间的区域。此操作将所需的数据样本数量从〜8000减少到625。最后一个阶段，一个用户界面使该模型能够使用给定的最小面积和粘度的给定用户输入来优化。洪水填充用于定义边界区域功能，以使最佳形状不会绕过最小区域。使用随机梯度Langevin Dynamics（SGLD）来确保在规避所需区域的同时优化最终形状。共同的，通过没有人类领域知识和适度计算开销的实用用户界面来探索具有极低阻力的形状。
Optimizing fluid-dynamic performance is an important engineering task. Traditionally, experts design shapes based on empirical estimations and verify them through expensive experiments. This costly process, both in terms of time and space, may only explore a limited number of shapes and lead to sub-optimal designs. In this research, a test-proven deep learning architecture is applied to predict the performance under various restrictions and search for better shapes by optimizing the learned prediction function. The major challenge is the vast amount of data points Deep Neural Network (DNN) demands, which is improvident to simulate. To remedy this drawback, a Frequentist active learning is used to explore regions of the output space that DNN predicts promising. This operation reduces the number of data samples demanded from ~8000 to 625. The final stage, a user interface, made the model capable of optimizing with given user input of minimum area and viscosity. Flood fill is used to define a boundary area function so that the optimal shape does not bypass the minimum area. Stochastic Gradient Langevin Dynamics (SGLD) is employed to make sure the ultimate shape is optimized while circumventing the required area. Jointly, shapes with extremely low drags are found explored by a practical user interface with no human domain knowledge and modest computation overhead.
